Decades of research in cognitive psychology has stressed the importance of categories, or concepts, to basic cognition. See for example, G. L. Murphy, The Big Book of Concepts, MIT Press, 2002. In the field of machine learning also, the task of predicting or classification is central. Moreover, the number of categories necessary for general human-level intelligence can easily exceed millions. It is likely that humans and higher animals acquire much of these categories or concepts on their own, via much experience and learning. Therefore, developing systems and methods that can learn many complex inter-related categories, in the millions and beyond, primarily on their own, would be very useful. This abundance of concepts, if effectively learned, has the potential to allow the system to make many useful distinctions in its lifetime, by repeatedly classifying input scenarios into appropriate categories and taking appropriate actions. Such flexibility of efficiently handling many categories is a necessity for sophisticated intelligence.
What is needed is a system and method that may learn many complex inter-related categories, in the millions and beyond, that may allow for making the required distinctions necessary for sophisticated intelligence.